SIRTA, a ground-based atmospheric observatory for cloud and aerosol research

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cloud and aerosol research

M. Haeffelin, Laurent Barthès, Olivier Bock, C. Boitel, S. Bony, Dominique Bouniol, H. Chepfer, Marjolaine Chiriaco, J. Cuesta, Julien Delanoë, et al.

To cite this version:

M. Haeffelin, Laurent Barthès, Olivier Bock, C. Boitel, S. Bony, et al.. SIRTA, a ground-based

atmospheric observatory for cloud and aerosol research. Annales Geophysicae, European Geosciences

Union, 2005, 23 (2), pp.253-275. �hal-00329353�

SRef-ID: 1432-0576/ag/2005-23-253

© European Geosciences Union 2005

Annales Geophysicae

SIRTA, a ground-based atmospheric observatory for cloud and aerosol research

M. Haeffelin ¹ , L. Barth`es ² , O. Bock ³ , C. Boitel ¹ , S. Bony ¹ , D. Bouniol ² , H. Chepfer ¹ , M. Chiriaco ¹ , J. Cuesta ¹ , J. Delano¨e ² , P. Drobinski ³ , J.-L. Dufresne ¹ , C. Flamant ³ , M. Grall ¹ , A. Hodzic ¹ , F. Hourdin ¹ , F. Lapouge ¹ , Y. Lemaˆıtre ² , A. Mathieu ¹ , Y. Morille ¹ , C. Naud ⁴ , V. No¨el ⁵ , W. O’Hirok ⁶ , J. Pelon ³ , C. Pietras ¹ , A. Protat ² , B. Romand ¹ , G. Scialom ² , and R. Vautard ¹

1 Laboratoire de M´et´eorologie Dynamique, Institut Pierre Simon Laplace, Ecole Polytechnique, 91128 Palaiseau Cedex, France

2 Centre d’Etudes des Environnements Terrestre et Plan´etaire, Institut Pierre Simon Laplace, 10-12 Avenue de l’Europe, 78140 Velizy, France

3 Service d’A´eronomie, Institut Pierre Simon Laplace, Universite Pierre et Marie Curie, 4, Place Jussieu, 75252 Paris Cedex 05, France

4 University College London, Geomatic Engineering, University College London, Gower Str, London WC1E 6BT, UK

5 Analytical Services and Materials, Hampton, VA 23666, USA

6 Institute for Computational Earth System Science, University of California, Santa Barbara, California, USA

Received: 3 September 2004 – Revised: 28 October 2004 – Accepted: 2 November 2004 – Published: 28 February 2005

Abstract. Ground-based remote sensing observatories have a crucial role to play in providing data to improve our under- standing of atmospheric processes, to test the performance of atmospheric models, and to develop new methods for future space-borne observations. Institut Pierre Simon Laplace, a French research institute in environmental sciences, created the Site Instrumental de Recherche par T´el´ed´etection Atmo- sph´erique (SIRTA), an atmospheric observatory with these goals in mind. Today SIRTA, located 20 km south of Paris, operates a suite a state-of-the-art active and passive remote sensing instruments dedicated to routine monitoring of cloud and aerosol properties, and key atmospheric parameters. De- tailed description of the state of the atmospheric column is progressively archived and made accessible to the scientific community. This paper describes the SIRTA infrastructure and database, and provides an overview of the scientific re- search associated with the observatory. Researchers using SIRTA data conduct research on atmospheric processes in- volving complex interactions between clouds, aerosols and radiative and dynamic processes in the atmospheric column.

Atmospheric modellers working with SIRTA observations develop new methods to test their models and innovative analyses to improve parametric representations of sub-grid processes that must be accounted for in the model. SIRTA provides the means to develop data interpretation tools for fu- ture active remote sensing missions in space (e.g. CloudSat Correspondence to: M. Haeffelin

([email protected])

and CALIPSO). SIRTA observation and research activities take place in networks of atmospheric observatories that al- low scientists to access consistent data sets from diverse re- gions on the globe.

Key words. Atmospheric composition and structure (Cloud physics; Aerosols and particles; Convective processes)

1 Introduction

The role of clouds remains a major uncertainty in current-day climate change simulations. Validation of these model sim- ulations requires increasingly comprehensive observations of clouds and their precursors, such as aerosols and water vapour (e.g. Lau and Crane, 1997; Tselioudis and Jakob, 2002; Williams et al., 2003; Bony et al., 2004). Along with expanding satellite programs that provide global coverage of an increasing number of key parameters of the climate sys- tem, ground-based observations continue to evolve and de- velop. The continuous nature of ground-based remote sens- ing observations makes them particularly suited to monitor fine scale processes that involve complex interactions be- tween clouds, aerosols, and radiative and dynamic processes.

Several programs have been successful in establishing

global or regional networks dedicated to monitoring a few at-

mospheric parameters, generally focusing on a single type of

remote sensing instrument (e.g. the Aerosol Research Sun-

Photometer Network, AERONET, Holben et al., 1998; the

European aerosol lidar network, EARLINET, Schneider et al., 2000; the Baseline Surface Radiation Network, BSRN, Ohmura et al., 1998). As researchers explore more and more complex processes, such as the life cycle of clouds and their diverse conditions of formation and dissipation, the necessity of operating highly instrumented observatories, sometimes called super-sites, becomes more and more evident. The de- velopment and operation of such facilities require a large community of experts in visible, infrared, and microwave remote sensing, using both high or narrow spectral resolu- tion and broad-band information, with both active and pas- sive remote sensing experience. To date, a limited number of programs have been able to establish long-term observatories that take advantage of the capabilities provided by extended instrument synergies (e.g. the U.S. Atmospheric Radiation Measurement Program, ARM, Stokes and Schwartz, 1994;

the Utah University based Facility for Atmospheric Remote Sensing, FARS, Sassen et al., 2001; the Dutch Cabauw Ex- perimental Site for Atmospheric Research, CESAR, Ulden and Wierenga, 1996).

Despite important progress and significant contributions from these programs (e.g. Ackerman and Stokes, 2003), ma- jor uncertainties remain in the quantification of the impact of clouds and aerosols on the global climate. The usefulness of such observatories for climate studies is best demonstrated when atmospheric modellers are actively involved. Routine and detailed monitoring of clouds and aerosols throughout the atmospheric column provide unique data-sets to evalu- ate the performance of atmospheric models and to develop parametric representations that more reliably simulate un- resolved processes (e.g. Morcrette, 2002; Guichard et al., 2003). Ground-based remote-sensing data point out pro- cesses that are not taken into account in current-day models (e.g. Hogan et al., 2002).

Ground-based observatories are also crucial for satellite observations. State-of-the-art remote sensing instruments operating in a coordinated manner on ground sites open the path to develop future space-borne missions. The per- formance of cutting-edge technology can be tested against well established standards and promising instrument syner- gies can be evaluated before a satellite mission reaches even phase A. Ground-based monitoring has been providing and continues to provide key validation data to satellite remote sensing missions (e.g. Sassen and Cho, 1992; Naud et al., 2003).

In an effort to provide a concrete solution to the need for better observation data sets, the “Site Instrumental de Recherche par T´el´ed´etection Atmosph´erique”, SIRTA, a French observatory dedicated to the remote sensing of clouds and aerosols was created around the research communities of Institut Pierre Simon Laplace (IPSL). IPSL is a French re- search institute in environmental sciences that federates six national research laboratories of the Paris metropolitan area, involved in both Earth observation from space and from the ground and in atmospheric modelling.

The site infrastructure and remote sensing instruments are described in Sect. 2. Section 3 provides information on the

SIRTA database. Section 4 presents research activities on at- mospheric processes related to clouds in the free troposphere and the boundary layer, which are illustrated through two case studies. Conclusions and prospective activities are given in Sect. 5.

2 SIRTA infrastructure

SIRTA is the atmospheric observatory of IPSL for cloud and aerosol research. The IPSL research laboratories dedicated to atmospheric research are:

– Centre d’´etudes des Environnements Terrestres et Plan´etaires (CETP)

– Laboratoire de M´et´eorologie Dynamique (LMD) – Laboratoire des Sciences du Climat et de l’Environne-

ment (LSCE)

– Service d’A´eronomie (SA).

At CETP, LMD, LSCE and SA scientists are involved in process study research, atmospheric modelling (climate, weather, chemistry and transport), satellite observation pro- grams, and atmospheric remote sensing from the ground (ac- tive and passive techniques). Development of remote sens- ing instruments has been an active area of research at IPSL laboratories for many years. Instruments such as radars, li- dars and radiometers for ground-based and airborne applica- tions were developed to observe atmospheric processes such as boundary layer dynamics, cloud formation and micro- physics, precipitation, aerosols and ozone in the urban en- vironment.

2.1 Remote sensing site

Latitude and longitude of the SIRTA observatory are 48.713 ^◦ N and 2.208 ^◦ E, respectively. SIRTA is located on the campus of Ecole Polytechnique in Palaiseau, a suburban community 20 km south of Paris. The geographical location of SIRTA in a worldwide context is shown in Fig. 1. The site infrastructure is described in Table 1. The observatory sits on a 10-km plateau about 160 m above sea level (see centre panel of Fig. 2). The plateau is a semi-urban environ- ment divided equally in agricultural fields, wooded areas, and housing and industrial developments. The prevailing winds are westerlies, blowing air of maritime origin over the site.

North-easterly winds occur quite frequently, as well advect- ing polluted air from the Paris metropolitan area over the site.

2.2 Instruments operating routinely at SIRTA

SIRTA is composed of an ensemble of state-of-the-art active

and passive remote sensing instruments, including radars, li-

dars, and radiometers. The measurement system was devel-

oped with sensor synergy in mind. Active remote sensing in-

struments provide information on the vertical distribution of

Fig. 1. Location of the SIRTA observatory on a World map in relation to other atmospheric observatories: ARM Program (USA), Chilboton Observatory (UK), and Cabauw experimental site (NL) described in Sect. 3.2.

Table 1. Infrastructure of the SIRTA observatory.

Table 1. Infrastructure of the SIRTA observatory

Site location Latitude 48.713º North Longitude: 2.208º East

Altitude 156m above mean sea level

Platforms Five 200-m2 platforms for transportable instruments

Elevated platform 500-m2 platform with unobstructed field of view 15 m above ground (roof of 3- story building). Dedicated acquisition room.

Mast 30-m mast for in-situ measurements (weather, turbulent, radiative fluxes) Lidar building Dedicated building for Rayleigh/Mie back-scattering lidar

Network 100 Mbit network available at all locations

particles in the atmospheric column (hydrometeors, aerosols) and their properties. Cloud radar and lidar emit waves at mil- limetre and micrometer wavelength, respectively. Their sen- sitivities with respect to the size distribution of particles are hence quite complementary. Passive remote sensing instru- ments measure the cumulative radiance contribution of the whole column. Spectral selection allows contributions from different constituents to be separated. The following sub- sections provide succinct descriptions of the instruments that constitute the core routine observations at the SIRTA obser- vatory. Instruments are listed in Table 2 and shown in Fig. 2.

2.2.1 Cloud and aerosol backscattering lidar

The LNA lidar (LNA stands for Lidar Nuages A´erosols) is

an Nd-Yag pulsed lidar developed at LMD for cloud and

aerosol remote sensing (Elouragini and Flamant, 1996). The

LNA lidar is shown in Fig. 2 (panel a). Laser emission is

a 20-Hz pulsed beam at 1064 nm, doubled at 532 nm and

linearly polarized. The laser beam is expanded to aug-

ment its diameter and to reduce divergence. Backscat-

tered photons are collected through a narrow field-of-view

telescope (NFOV, 0.5 mrad) and a wide field-of-view tele-

scope (WFOV, 5 mrad) that range 2–15 km and 100 m–5 km,

Fig. 2. Photographs the main components of the SIRTA observatory: overall view of the Ecole Polytechnique campus (center panel), measurement platforms (top panel), (A) LNA lidar, (B) RASTA radar, (C) ceilometer, (D) surface radiative flux station, (E) Drakkar MWR, and (F) Sun-photometer.

Table 2. List of instruments operated routinely or continuously at SIRTA.

Haeffelin et al. Page 1 sur 5

Table 1. Infrastructure of the SIRTA observatory

Site location Latitude 48.713º North Longitude: 2.214º East

Altitude 156m above mean sea level

Platforms Five 200-m2 platforms for transportable instruments

Elevated platform 500-m2 platform with unobstructed field of view 15 m above ground (roof of 3- story building). Dedicated acquisition room.

Mast 30-m mast for in-situ measurements (weather, turbulent, radiative fluxes) Lidar building Dedicated building for Rayleigh/Mie back-scattering lidar

Network 100 Mbit network available at all locations

Table 2: List of instruments operated routinely or continuously at SIRTA

Instrument Range

(V: vertical; S:

scan)

Area of use Instrument PI (Institute)

Backscattering lidar (532,

1064 nm) 0.1 –15 km (V*) Cloud and aerosol

properties C. Pietras (LMD) 95 GHz doppler radar 0.1 – 15 km (V) Clouds properties A. Protat (CETP) Ceilometer (Impulsphysics

LD40) 0.1 – 6 km (V) Cloud height H. Baltink (KNMI)

BSRN radiometric station Surface Surface radiation budget M. Haeffelin (IPSL) Microwave radiometer

(20+30GHz)

Column

integrated Vapor + liquid water L. Barthès (CETP) Aeronet/Photons sun-

photometer

Column

integrated Aerosols, water vapor P. Goloub (LOA) Meteorological station Standard 2 and

10 m

Surface

thermodynamics C. Pietras (LMD) Radiosondes (Météo-France) 0 – 30 km (V) Vertical wind+PTU

profiles

M. Ruchon (Météo- France)

*Range depends on time averaging

Table 3: List of instruments developed at IPSL and collaborating institute that operate at SIRTA in the context of specific field campaigns

Instrument

Range

(V: vertical; S:

scan)

Area of use Instrument PI (Institute)

Doppler Infrared Lidar (10.6

? m) 0.3 –10 km (S*) Dynamics P. Drobinski (SA/LMD)

5 GHz Doppler Radar 0.5 – 100 km (S) Precipitation G. Scialom (CETP) DIAL Lidar (266/289/316nm) 0.1 – 7 km (V*) Ozone, aerosols G. Ancellet (SA) Raman Lidar (355 nm) 0.1 – 7 km (S*) Water vapor O. Bock (SA/IGN) Infrared radiometer (8, 11, 12

µm) Column integrated Brightness

temperature G. Brogniez (LOA/CIMEL) Spectro-pluviometer Surface Precipitation J-Y. Delahaye (CETP)

*Range depends on time averaging

respectively, with a vertical resolution of 15 m. Linearly po- larized and cross-polarized photons at 532 nm are detected using two separate optical detection systems.

Vertical distributions of particles are fully characterized from the ground to about 15 km and the structure of the atmo- sphere, such as the boundary layer height and the altitudes of aerosol and cloud layers are retrieved. The raw signal from the lidar is not calibrated in an absolute sense, as it depends on the laser emission power. To retrieve particle backscatter- ing or extinction coefficient profiles, the lidar profile must be calibrated. The lidar calibration is performed by normaliz- ing the lidar profile to a computed molecular backscattering profile in a particle-free zone of the troposphere above the boundary layer, typically between 3 and 6 km altitude. The molecular backscattering profile is derived from tempera- ture and pressure profiles provided by collocated radio-sonde data. The cross-to-linear polarization ratio is used to identify non-spherical particles; in case of cloud layers the ratio is used to separate ice from liquid water. In case of aerosols, the ratio is used to identify larger dust particles. The LNA li- dar participated in a lidar inter-calibration campaign carried out in the framework of the European EARLINET project to test system alignments (Matthias et al., 2004).

LNA lidar data are used extensively in cloud and aerosol studies and to validate cloud detection from satellite observa- tions. Chepfer et al. (1999, 2000) carried out studies to val- idate cirrus cloud parameters (cloud top height, occurrence, thermodynamic phase) inferred from the polarized radiome- ter POLDER-1 measurements. Similarly, Naud et al. (2004) performed a detailed analysis of cloud detection retrievals by multi-angle and high-spectral radiometers on board satel- lites. In the period 2001–2003, the LNA lidar participated in the European EARLINET project dedicated to monitoring aerosols in the atmospheric column using 21 lidar stations in Europe (e.g. Ansmann et al., 2003).

2.2.2 95 GHz Cloud Doppler Radar (RASTA)

The RASTA (Radar A´eroport´e et Sol de T´el´ed´etection Atmo- sph´erique) cloud radar operates at SIRTA to document the microphysical and dynamic properties of all types of non- precipitating clouds. RASTA is a vertically-pointing single- beam 95-GHz Doppler radar (see Fig. 2, panel b). The sys- tem is installed in a van, and hence transportable. The beam width is 0.18 ^◦ , the sensitivity is about 51 dBZ at 1 km and the 1.2-m Cassegrain antenna is vertically pointing. The RASTA radar is also designed for airborne applications (using a dif- ferent antenna and a dual beam configuration) and was in- volved in several field campaigns. The ground-based con- figuration of the RASTA cloud radar operates routinely at SIRTA since October 2002.

Absolute calibration of the RASTA radar was performed during an inter-calibration campaign held at the Chilbolton (United Kingdom) and Cabauw (Netherlands) observatories in the February–March 2004 period, in the framework of the CloudNet project, a European pilot network of stations for observing cloud profiles (see CloudNet reference). A

first comparison was carried out against the Chilbolton 94- GHz Galileo radar. Galileo is itself calibrated against 3 and 35 GHZ radars. Galileo calibration constants are checked regularly using radar echoes in light rain (between 3 and 10 mm/h) based on the fact that attenuation by the light rain produces returned power for lower gates that are constant to within 1 dBZ (Hogan et al., 2002). A second compari- son was carried out against the 35-GHz Doppler radar of the Cabauw observatory to check the consistency with the abso- lute calibration obtained from the Chilbolton observatory. As a result, we reached a 1-dBZ consistency between the three CloudNet millimetre-wave radars.

RASTA is devoted to the investigation of cloud processes, through the documentation of the microphysical, radiative, and dynamical cloud properties, using either radar-lidar syn- ergetic algorithms (Tinel et al., 2005) or radar-only meth- ods (Protat et al., 2003). The other objectives are to validate space-borne observations and the representation of clouds in atmospheric models, ranging from the explicit representation of clouds in cloud-resolving models to the cloud parameteri- zations in weather forecast and climate models.

2.2.3 Surface radiative flux station

A Kipp & Zonen (KZ) AP-2 solar tracker was installed at SIRTA in December 2002 to monitor the downwelling so- lar and infrared components of the surface radiation budget (Fig. 2, panel d). The tracker carries a CH1 pyrheliometer, a shaded CM22 pyranometer, and a shaded CG4 pyrgeome- ter. The pyrheliometer measures the direct or un-scattered solar radiation (0.3–4.0 µm), while the shaded pyranometer measures the downwelling diffuse solar radiation scattered by the atmosphere (0.3–4.0 mm). The two measurements are then combined to produce the total solar radiation incident at the surface, as recommended by the Baseline Surface Radia- tion Network (Ohmura et al., 1998). The shaded pyrgeometer measures the downwelling infrared radiation incident at the surface (4.0–40 µm).

The instruments were factory calibrated by Kipp & Zo- nen in 2002. The pyrheliometer is calibrated against an open cavity absolute radiometer (secondary standard) that is it- self calibrated every five years against the world radiomet- ric reference, maintained by the World Radiometric Center (WRC) in Davos, using the Sun as source (Direct Radiation).

The pyranometer is calibrated against a secondary standard.

An inter-comparison between 15 pyranometers (Michalsky et al., 2003) shows that the KZ factory calibration and instru- ment performance are very satisfactory (1 W/m ² root mean square error). Comparisons of pyrgeometers performed at the ARM Oklahoma and Alaska sites revealed very good consistency between the KZ CG4 and the WRC absolute sky- scanning radiometer (Philipona et al., 2001). Recalibration of solar instruments was performed on site in June 2004 us- ing a PM06 absolute cavity radiometer, using an alternating shading-unshading technique described in Philipona (2002).

The CG4 instrument calibration will be calibrated periodi-

cally (every other year) by the WRC.

2.2.4 Two dual-wavelength micro-wave radiometers The Drakkar Microwave Radiometer (D-MWR) is a verti- cally pointing system developed at CETP that measures ra- diometric brightness temperature in K and Ka bands (see Fig. 2, panel e). The antenna beam-width is 13 ^◦ for the 23.8- Ghz channel and 11 ^◦ for the 36.8-Ghz channel. Radiomet- ric accuracy is about 1 K. The Rescom Microwave Radiome- ter (R-MWR) has a two-axis scanning system. The antenna bandwidth is 1.9 ^◦ . This micro-wave radiometer pair has been used in combination with a spectro-pluviometer to investi- gate the individual contributions of rain, water vapour, and liquid water at frequencies ranging from 10 GHz to 90 GHz for telecommunication applications.

The D-MWR operates at SIRTA on a continuous mode since January 2001. Thermal protection of the instrument was improved in July 2002 to minimize internal tempera- ture fluctuations. Calibration of the D-MWR is done using the operational radiosondes launched twice daily by M´et´eo- France (see Sect. 2.2.6). For each clear-sky episode, syn- thetic brightness temperatures are computed at the D-MWR frequencies from the radiosonde data using the Kummerow and Weinman (1988) radiative transfer code and compared to the measured brightness temperatures, in order to derive the calibration constants. Absolute calibration is carried out periodically by transferring the R-MWR calibration to the D-MWR. Absolute calibration of the R-MWR is obtained using a tipping-curve technique based on measurements at two or more viewing angles (Han and Westmaster, 2000).

In this method, the relationship between atmospheric opac- ity and viewing angle is used to estimate cosmic brightness temperature. The difference between this estimation and the true value (2.7 K) is used to derive the calibration of the mi- crowave radiometer.

The D-MWR participated in several field experiments such as the study of ocean-atmosphere coupling and cyclo- genesis in the Northern Atlantic, as part of the Fronts and Atlantic Storm-Track Experiment (FASTEX). The D-MWR was used in part to validate satellite retrievals from the Spe- cial Sensor Microwave/Imager (SSM/I, Eymard, 2000).

2.2.5 Multi-wavelength Sun-photometer

A CIMEL 318-CE Sun-photometer was installed at SIRTA in July 2002, as part of the PHOTONS/AERONET network (Holben et al., 1998). The PHOTONS program is in charge of 25 AERONET sites. The Sun-photometer consists of an optical device mounted on a robot to track the solar disc.

A control box receives the collected data and is linked to a transmitter coupled to an antenna that transmits the data through a Meteosat transmission channel. The whole sys- tem is powered by batteries coupled to solar panels, allowing for automatic continuous operation (see Fig. 2, panel f). The Sun-photometer data are transmitted hourly to NASA GSFC for analysis and become available on the AERONET web site soon thereafter. A cloud mask is applied to the data to remove cloud contamination (Smirnov et al., 2000). Aerosol

optical depth is then provided at four wavelengths (440, 670, 870, and 1020 nm). In the case of mostly clear conditions, the Sun-photometer measures sky radiances from which aerosol size distributions can be retrieved. Furthermore, polariza- tion measurements are carried out at 870 nm to complete the aerosol characterizations. Measurements at 940 nm are also realized to retrieve the integrated water vapor content of the atmosphere. SIRTA technicians perform daily oper- ational checks, such as instrument status and transmission of collected data, and check the cleanliness of the optics on a weekly basis. Long-term maintenance and calibration are performed by the PHOTONS program to ensure consistency with the AERONET database.

2.2.6 Weather station and radiosonde profiles

In-situ measurements at SIRTA currently consist of pressure, temperature, humidity, wind direction and speed, and pre- cipitation. Those measurements are performed on the roof platform, on 2-m and 10-m masts. Precipitation data (rate and cumulative) are obtained by a tipping-bucket pluviome- ter. A second set of sensors was purchased in mid-2004, including temperature, humidity and pressure sensors, in- stalled at 2 m above ground, as well as a wind vane and anemometer installed at 10 m above ground. The 10-m level is equipped with a sonic anemometer for high-frequency measurements of heat and moisture fluxes. Radiosonde launches are performed by the French national weather ser- vice (M´et´eo-France) twice daily at 00:00 and 12:00 UT from the Trappes regional weather centre, 15 km west of SIRTA, as part of the M´et´eo-France operational upper-air sounding network.

2.3 Instruments operating in the framework of field cam- paigns

SIRTA hosts additional active and passive remote sensing in- struments that operate during intensive observation periods.

They are usually personnel intensive and cannot be operated continuously. These instruments are listed in Table 3. The doppler lidar and radar presented below are established at SIRTA since 1999.

2.3.1 Transportable wind lidar

The LVT (Lidar Vent Transportable or Transportable Wind Lidar, TWL) is a Doppler coherent lidar at 10.6 µm, with a typical range resolution of 300 m in line-of-sight (LOS).

The maximum range is greater than 10 km shot-to-shot in

horizontal LOS. The LVT lidar can detect cirrus clouds (8

to 11 km) in vertical LOS. The LVT lidar measures a range

resolved line-of-sight wind component. Different scans can

be programmed: plan-position indicator (PPI) (scan at fixed

elevation angle), range-height indicator (RHI, scan at fixed

azimuth angle), conical scans or series of regularly spaced el-

evation and azimuth angles (raster scan). Wind components

can be retrieved from raster-scan data. The limitations for

operating conditions are rain, fog and low cloud layers.

Table 3. List of instruments developed at IPSL and collaborating institute that operate at SIRTA in the context of specific field campaigns.

Table 3: List of instruments developed at IPSL and collaborating institute that operate at SIRTA in the context of specific field campaigns

Instrument Range

(V: vertical; S:

scan)

Area of use Instrument PI (Institute) Doppler Infrared Lidar (10.6

µm) 0.3 –10 km (S*) Dynamics P. Drobinski (SA/LMD)

5 GHz Doppler Radar 0.5 – 100 km (S) Precipitation G. Scialom (CETP) DIAL Lidar (266/289/316nm) 0.1 – 7 km (V*) Ozone, aerosols G. Ancellet (SA) Raman Lidar (355 nm) 0.1 – 7 km (S*) Water vapor O. Bock (SA/IGN) Infrared radiometer (8, 11, 12

µm) Column integrated Brightness

temperature G. Brogniez (LOA/CIMEL) Spectro-pluviometer Surface Precipitation J-Y. Delahaye (CETP)

*Range depends on time averaging

The LVT was used to investigate the convective atmo- spheric boundary layer during the ECLAP campaign (Etude de la Couche Limite en Agglom´eration Parisienne, Drobin- ski et al., 1998), and the atmospheric boundary layer in complex terrain like mountainous regions during the Meso- scale Alpine Program (MAP) (Drobinski et al., 2003), or urban area and coastal area (Bastin et al., 2005) during the ESCOMPTE campaign (Exp´erience sur site pour contrain- dre les mod`eles de pollution atmosph´erique et de transport d’´emissions).

2.3.2 5-GHz dual polarization Doppler radar (RONSARD) RONSARD is a C-band ground-based pulsed radar aiming mainly at documenting the dynamic properties of precipitat- ing systems in the troposphere (reflectivity, wind and vari- ance of the wind). This instrument has a 4-m diameter an- tenna on a trailer. This antenna bears both transmitter and receiver in order to avoid energy losses. The antenna can be programmed to scan consecutive cones at various elevations and various elevation steps. The scan duration is about 30 s for a complete 360 scan in azimuth at fixed elevation which results in about 9 min for a complete volume scan (20 eleva- tions). It can also perform RHI scans at fixed azimuth. The maximal range is 100 or 200 km, depending on pulse repeti- tion frequency, with corresponding unfolded velocity +/−20 or +/−10 m/s, and range resolutions 200 or 400 m, respec- tively.

Originally, there were two radars until 1990. Since then, only one radar was maintained and improved in order to ful- fill another scientific objective, namely the detailed descrip- tion of the boundary layer in clear air or under cloudy con- ditions, even in the absence of precipitation. In order to an- swer this second objective while preserving the first one, the real-time signal processing was modified in 1998. Two cam- paigns for observing the boundary layer under summer con- ditions were conducted in 1993 and 1998 (Turbulence Radar

Fig. 3. Types of operations (continuous or routine measurements) for each instrument listed in Table 2.

Aviation Cells, TRAC 93 and 98).

RONSARD (with one or two radars) was the key instru- ment of several national or international campaigns devoted to deep convection under the tropics (Tropical Deep Con- vection, COPT 81 in Ivory Coast), to mid-latitudes fronts (FRONTS spring 1984 and FRONTS fall 1987; Lemaˆıtre et al., 2001) and mid-latitude convection over complex orogra- phy (MAP; Tabary and Scialom, 2001).

A third scientific objective concerns the interactions be-

tween microphysics and dynamics, and the role of the ice

phase in the organization of precipitation. In order to fulfill

this third objective, the Dual polarization (horizontal and ver-

tical) capability is presently being added to RONSARD by

means of a second receiver and antenna modification. The

additional polarimetric quantities measured are the differen-

tial reflectivity, the differential phase shift, and the correla-

tion coefficient at zero lag. Processing these parameters by

means of specific algorithms allows us to identify hydrome-

teors. The dual polarization capability will be operational in

2004.

3 SIRTA data

Figure 3 shows the type of operations achieved for each in- strument listed in Table 2 since 2001. Routine LNA lidar measurements (2 days per week) were initiated in 2001 and increased to 4 days per week in October 2002 when rou- tine RASTA radar operations began. One year later, 24-h round-the-clock radar operation capability was implemented.

In summer 2002, the Drakkar microwave radiometer was re- conditioned to improve temperature stability for retrieval of water vapour and liquid water content. A sunphotometer was installed, exploiting the visible and near-infrared end of the spectrum to monitor aerosol properties. In 2003 a ceilometer started to provide continuous day-night monitor- ing of cloud base height. Simultaneously, a radiometric sta- tion was installed to measure the surface radiation budget with instruments designed for long-term monitoring. Surface thermodynamic conditions are monitored at the site since 2001. M´et´eo-France radiosonde profile data are archived since 2001.

3.1 SIRTA data base

The data system is designed with three objectives in mind:

(1) data quality, (2) timely processing, and (3) data ac- cess. Table 4 lists the level-1 data products available in the SIRTA archive. Quality control tests are applied to each data stream. Near-real-time (about one hour after acquisi- tion) processing is performed on the LNA lidar and RASTA radar data streams. Resulting quick-look images appear in- crementally on the SIRTA webpage as data is being collected (http://sirta.lmd.polytechnique.fr). Level-1 data (data files + images) become accessible to scientific users on a day+1 ba- sis through file transfer protocol access. Access information is provided on the SIRTA webpage.

Table 5 describes level-2 products that are currently devel- oped by the scientific community involved in SIRTA. Algo- rithms labelled “OPE” are mature algorithms that are or can be applied on an operational basis to large data-sets. Algo- rithms labelled “RES” are research algorithms, published or unpublished. “RES” algorithms are applied to selected situ- ations (e.g. thin ice clouds for the lidar-IR microphysics al- gorithm and thick ice clouds for the radar-lidar microphysics algorithm). Comparison of retrievals based on our algorithms to retrievals by others is part of on-going research.

3.2 SIRTA data in international databases

Integration of SIRTA data in international reference databases is actively pursued. The objective is to reach and maintain international quality standards. SIRTA contributes surface radiation data to the BSRN database (Ohmura et al., 1998). Sun-photometer measurements are part of AERONET (Holben et al., 1998). Lidar measurements are integrated in the EARLINET data set (Schneider et al., 2000). Radar and lidar data are part of the CloudNet pro- gram.

SIRTA participates in the tropospheric profiling working group of the Global Energy and Water Cycle Experiment (GEWEX) Radiation Panel to enhance cooperation between advanced atmospheric profiling sites. The mission of this network is “to collect consistent data sets of known calibra- tion and quality of the vertical structure of clouds, aerosols, and water vapour, to study their radiative impact.” Extended climatic regions are covered with sites in the maritime and continental mid-latitudes: Cabauw (Netherland), Chilbolton (United Kingdom), L’Aquila (Italy), Lindenberg (Germany), Palaiseau (France), and Lamont, Oklahoma (United States);

in the Arctic: Barrow, Alaska (United States); and in the Tropics: Darwin (Australia) and Naru Island (United States).

3.3 Educational outreach

Each year, over one hundred students from undergraduate and graduate physics, climate and environment programs ex- pand their knowledge on atmospheric remote sensing dur- ing experimental work sessions at SIRTA. Students learn about the technical aspects of remote sensing instruments and participate in the acquisition of atmospheric measurements.

Then they perform data analysis on those measurements and discuss capabilities and limitations of the systems. Data in- terpretation tutorials are also offered to scientists and grad- uate students that are interested in using SIRTA data. These tutorials are available for the algorithms described in Table 5.

4 SIRTA research

4.1 SIRTA research objectives

Clouds are the main focus of SIRTA research. Clouds devel- oping in the free troposphere are studied extensively at the large scale using passive remote sensing satellite data. Ex- ploiting active remote sensing and multi-spectral synergies, SIRTA research focuses on studying vertical distributions of cloud occurrence, cloud particle shape and size, water con- tents of clouds and cloud internal dynamics. The retrieval methods recently developed in preparation for the upcoming Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Ob- servations (CALIPSO) and Cloud Radar Pathfinder satellite mission (CloudSat) are described in Sect. 4.2. The dynam- ics and thermodynamics of the boundary and surface lay- ers are key factors of the formation and life cycle of clouds.

The experience acquired in studying turbulent and organized transport of water and energy near the surface is used to test the capability of state-of-the-art parametric representations of boundary layer processes in atmospheric models. Sec- tion 4.3 presents a method developed to identify sources of uncertainties in such parametrizations.

As all research activities currently pursued at IPSL and

collaborating institutions exploiting SIRTA data cannot be

presented in this paper, we list the main topics of research in

Table 6. All studies have a common denominator in that they

require development of new methods to take advantage of

instrument synergies (“SYN” column in Table 6). Extensive

Table 4. Level-1 data accessible in the SIRTA archive. *Sun-photometer data should be retrieved directly from AERONET.

Haeffelin et al. Page 2 sur 5

Table 4. Level-1 data accessible in the SIRTA archive. *Sun-photometer data should be retrieved directly from AERONET.

Instrument Level-1 products Quality-control procedures Data access

Back- scattering Lidar

• Range-corrected, non-normalized back- scattered power (PR

, proportional to m

sr

1

)

• Quality flag

• Removal of electronic noise in analog signal

• Removal of atmospheric background contribution

• Identification of geometrical limitation at near range

• Identification of statistical errors related to signal-to-noise ratio

VISU: NRT DATA: D+1

95 GHz Radar

• Reflectivity (DBz)

• Velocity (m/s)

• Variance

• Removal of speckle noise

• Limitation to 15-km range VISU: NRT

DATA: D+1

Ceilometer

• Range-corrected, non-normalized back- scattered power (PR

, proportional to m

sr

1

)

• Quality flag

• Removal of speckle noise

• Limitation to 8-km range VISU: D+1

DATA: D+1

Radiometric Station

Surface downwelling irradiances (W m

):

• Direct solar

• Diffuse solar

• Global solar

• Longwave

Procedures as per BSRN recommendations:

• Test physically possible limits

• Test extremely rare limits

VISU: D+1 DATA: D+1

Microwave radiometer

• Brightness temperature

VISU: D+1 DATA: D+1 AERONET

Sun-

photometer*

• Radiance (W m

sr

) QC checks performed by AERONET VISU: D+1 DATA: D+1

Weather station

• Temperature

• Pressure

• Relative humidity

• Wind module and direction

• Precipitation rate

Test physically possible limits VISU: D+1 DATA: D+1

Meteo-France radiosonde data

Profiles of temperature, pressure, relative humidity, and wind

QC checks performed by Météo-France VISU: D+1 DATA: D+1

partnerships have been setup between SIRTA and the mod- elling research community. This involves researchers that are trained at the model-observation interface and able to provide feedback between models and observations (studies marked in the “MOD” column, Table 6). Similarly, researchers from the satellite community are actively involved in the activities of the observatory, so that it can serve as a preparation plat- form for future satellite missions (see studies marked “SAT”

in Table 6).

4.2 Cirrus cloud study

Cirrus clouds have low temperatures and are often semi-

transparent. Because of these characteristics, they contribute

significantly to the natural greenhouse effect, and influence

the global cloud radiative equilibrium. Despite important

progress in the last 15 years, the quantification of cirrus cloud

radiative impact is still unknown. One of the main causes

of uncertainty comes from our partial knowledge of their

microphysical properties that strongly impact the ice cloud

Table 5. Retrievals of atmospheric properties (level-2 data) from single or multiple instruments require developments of specific algorithms and are part of active areas of research. *Sun-photometer data should be ordered directly from AERONET.

Haeffelin et al. Page 3 sur 5

Table 5. Retrievals of atmospheric properties (level-2 data) from single or multiple instruments require developments of specific algorithms and are part of active areas of research. *Sun-photometer data should be ordered directly from AERONET.

Instrument/

data stream

Level-2 products Algorithm Pro-

cess

Cloud and aerosol vertical structure

Multi-test algorithm applied on 532-nm channel to identify cloud layers, aerosol layers, molecular layers, and boundary layer height (Morille et al., 2004)

OPE

Optical depth

Multi-retrieval algorithm applied on 532-nm channel to retrieve optical depth of cloud or aerosol layers (Cadet et al., 2004)

Back-scattering OPE Lidar

Depolarization and color ratio

Multi-wavelength algorithms using linear and cross-polarized 532-nm and linear 1064-nm channels to discriminate particle shape (Noel et al., 2002)

OPE

95 GHz Radar

Cloud structure Ice/water content Particle size distribution

Mean particle diameter from radar reflectivity and doppler velocity

Size distribution related to mean diameter Extinction and ice water content function of reflectivity and size distribution

Retrieval uncertainties estimated 50%

Ceilometer Cloud-base height Vaisala proprietary algorithm OPE Radiometric

Station

Fraction of cloud cover Shortwave and longwave clear-sky fluxes

Clear-sky models derived from measurements.

Threshold to identify cloud cover fraction.

(Long and Ackerman 2000)

OPE

Microwave radiometer

Integrated water vapor and liquid water content

Brightness temperatures simulated from

radiosonde profiles to calibrate MWR. Water vapor and liquid water contents inverted using the Kummerow and Weinman (1988) algorithm.

OPE AERONET

Sun-

photometer*

Optical depth Angström coefficient Size distribution

See Holben et al. (1998) OPE

Instrument Synergies

Lidar + narrowband infrared radiometer

Ice-cloud microphysics

Improved split-window technique using lidar-IR synergies to retrieve cirrus cloud particle size and shape (Chiriaco et al., 2004)

RES Cloud vertical structure Combination of radar and lidar cloud masks OPE Cloud structure

Ice/water content Particle size distribution

Normalized particle size distribution No* (with respect to mean diameter)

Lidar extinction: α=s No*

Z

Ice water content: IWC=a No*

Z

(Tinel et al., 2004)

RES Radar + Lidar

Particle terminal velocity

Vertical air velocity

Terminal velocity function of radar reflectivity and particle shape

Doppler velocity= air + terminal velocity (Protat et al., 2003)

RES

M. Haeffelin et al.: SIRTA, an observatory for cloud and aerosol research 263 Table 6. Description of main research activities exploiting SIRTA data. “SYN” identifies studies that include developing methods in instrument synergy; “MOD” labels those involving model-to-observation comparisons; “SAT” indicates studies that require satellite data or that develop techniques that can be used on satellite data for large-scale applications.

Haeffelin et al. Page 4 sur 5

Table 6. Description of main research activities exploiting SIRTA data. “SYN”

identifies studies that include developing methods in instrument synergy; “MOD”

labels those involving model-to-observation comparisons; “SAT” indicates studies that require satellite data or that develop techniques that can be used on satellite data for large-scale applications.

Topic Description SYN MOD SAT

Cloud radiative impact and life cycle:

• Development of new remote sensing techniques based on instrument synergies to retrieve cloud macrophysical, microphysical, and dynamic properties (Noel et al., 2002;

Chiriaco et al., 2004; Tinel et al, 2004).

• Application to large ground-based datasets and future satellite missions

X X X

Representation of cloud properties in atmospheric models:

• Cloud microphysical properties in meso-scale models

• Cloud microphysical and dynamic properties in numerical weather prediction models (European CloudNet project)

X X

Photochemistry and clouds:

• Study of the representation of clouds and their impact on photolysis in Chemistry-Transport models (Vautard et al., 2001)

X X

Water vapour and clouds:

Water Vapour Profiling Inter-Comparison field experiment (VAPIC)

• Ground and satellite remote sensing synergies to improve water vapor retrievals

• Study of cloud formation processes (role of BL dynamics, water vapor, and aerosols) of mid-latitude low-altitude clouds

X X X

Cloud overlap in GCMs:

• Exploiting radar-lidar synergies to derive new cloud overlap assumptions for GCM sub-grid parametrizations.

X X

Clouds and radiation in the regional climate:

• Regional climate study based on cloud and surface radiation monitoring data. Detection of anthropogenic signals.

X X X

Clouds

Validation of satellite cloud property retrievals:

• Cloud height and thermodynamic phase retrieved by POLDER- 1 and POLDER-2 (Chepfer et al., 1999, 2000)

• Cloud top height retrieved by MISR and MODIS (Naud et al, 2004)

• Semi-transparent clouds retrieved by SEVIRI/MSG

X X

radiative budget. In particular, the particles’ size, shape and orientation in space have to be well documented in order to quantify correctly the ice cloud radiative impact. These properties vary widely from one situation to another, depend- ing on the thermal and dynamic conditions under which the cloud was formed (e.g. jet cirrus, front cirrus, contrails). The following subsections describe the observation and interpre- tation tools developed at SIRTA to characterize cloud opti- cal, microphysical and dynamical properties and ultimately to better understand the life cycle of clouds.

4.2.1 Observing cirrus clouds

Figure 4 shows a frontal passage observed by the RASTA radar (radar reflectivity, panel a) and the LNA lidar (lidar backscattered power, panel b) during 8 h on 1 April 2003.

Both instruments show the typical evolution of the vertical

distribution of clouds associated with mid-latitude fronts,

characterized by optical-thin cirrus clouds ahead of the front

followed by developing cumulonimbus clouds that eventu-

ally produce precipitation. Figure 4c shows a cloud mask

derived from the combined analysis of the radar reflectiv-

ity and the lidar backscattered power. Clouds shown in yel-

low, green and red are detected by lidar only, radar only, and

both instruments, respectively. Clouds shown in grey cor-

respond to areas where one instrument detects clouds while

the other detects cloud-free air. The cloud mask reveals that

the lidar provides a full characterization of the vertical ex-

tent of the cirrus cloud (07:00 to 12:00 UT), but as the cloud

becomes optically thicker, the lidar signal is attenuated and

the range is limited to the lowest 2 km of the cloud. The

cloud mask shows that the radar reflectivity can vary greatly

in cirrus clouds (−40 to −20 dBZ), but becomes very strong

(a)

(b)

Fig. 4. Cloud layers observed at SIRTA on 1 April 2003. Vertical cross sections of (a) RASTA cloud radar reflectivity (dBZ), (b) LNA lidar backscattered power (proportional to m ⁻¹ sr ⁻¹ ), (c) cloud mask derived from radar-lidar synergies (green: radar only; yellow: lidar only;

red: radar+lidar retrieval; grey: discrepancy between lidar and radar retrieval; other: no cloud detected).

(c) Fig. 4. Continued.

in mixed-phased or precipitating clouds (12:00–15:00 UT).

Overlap of radar and lidar signals exist (red cloud mask) but the cloud mask reveals that radar-lidar synergy is particularly suited to extend the range of observable cloud layers.

4.2.2 Cirrus cloud microphysical properties

Infrared wavelengths are commonly used to retrieve parti- cle size from satellite passive remote sensors, as those wave- lengths are highly sensitive to the amount of ice present in the cloud. The classical split-window technique (Inoue, 1985), as well as more recent advanced techniques (Minnis et al., 1998; Baum et al., 2000; Platnick et al., 2003), are based on differences of absorption in various infrared channels, some- times completed by visible channels. We use new retrieval techniques based on active and passive remote sensing syn- ergies that will become available at the global scale with the future CALIPSO and CloudSat satellite missions.

• Vertical distribution of particle shape is retrieved from LNA lidar depolarization signals. This technique is based on the strong sensitivity of the state of polariza- tion of the light in the visible domain to the shape of the scatterers. The light emitted by the laser of the li- dar is initially linearly polarized, and the scattering by ice crystals changes the state of polarization of the light that is recovered by the lidar telescope. This sensitiv- ity has been used for years (Sassen, 1991) for discrimi- nating water clouds (spherical particles) and ice clouds (non-spherical crystals). We apply the algorithm devel- oped by Noel et al. (2002) that will be used to process CALIPSO lidar data at the global scale. It uses lidar depolarization to classify the ice crystal shapes in four different categories, depending on their shape ratio Q, the ratio between the ice crystal length and width, rang- ing from plates (small Q) to columns (large Q). Shape ratio classes correspond to asymmetry factor intervals in the visible. The classification method uses simple comparisons between the measured linear depolariza- tion rate and the simulated one for ice cloud composed

of crystals with different shape ratios. The simulated li- dar depolarization ratio is based on a ray-tracing code that includes multiple scattering phenomena. The com- parison between the observed and simulated depolariza- tion rate allows the variability of the ice crystal shape ratio Q within the cirrus cloud to be obtained.

Figure 5 shows the LNA lidar depolarization ratio, the cloud optical depth and particle shape classification for the case of 1 April 2003. In the cirrus cloud (before 12:00 UT) the depolarization ratio is greater than 50%.

It ranges between 15 and 30% at the base of the cu- mulonimbus cloud. The optical depth, derived from the attenuation of the LNA lidar backscattered power at cloud top compared to a theoretical return in the ab- sence of the cloud (Cadet et al., 2004), is close to 1 in the cirrus cloud. Figure 5c shows that the cirrus cloud is predominantly constituted of columns, with flatter par- ticles towards the base of the cloud. The base of the cumulonimbus cloud is formed of flat particles (plates).

When depolarization is very low (less than 10%) no particle shape is retrieved, indicating a possible mixed- phase part of the cloud.

• Mean cloud layer particle size is retrieved with a split window technique improved by multi-wavelength re- trieval constraints (8.7, 10.5 and 12 µm) complemented by LNA lidar constraints: (i) cloud vertical range de- tection based on 532-nm backscattered lidar signal and (ii) shape constraint from 532-nm lidar depolarization.

The lidar backscattered signal is used to detect the cir-

rus cloud, to determine its base and top altitude, and

to estimate its top and base temperatures using an ob-

served temperature profile. First, the IR radiances, com-

bined with the lidar backscatter information, lead to sev-

eral values of effective radius, depending on the particle

shape hypothesis. Then, the lidar depolarization obser-

vation is used as a constraint in selecting the best guess

for the particle shape ratio and hence the more reliable

effective size.

Fig. 5. Cloud properties observed on 1 April 2003: (a) LNA lidar depolarization ratio (ratio of cross-polarization to linear-polarization backscattered signal); (b) cloud layer optical depth; and (c) particle shape classification. Shape ratio classes: I=Q<0.05 (plates);

II=0.05<Q<0.7, III=0.7<Q<1.1; IV=Q>1.1 (columns).

For the 1 April 2003 case, the lidar-derived cloud base and top temperatures are 224 K and 214 K, respectively, over a 1-h period centered on the Terra satellite overpass time (10:20 UT indicated by a vertical line in Fig. 4).

The brightness temperature from the MODerate resolu- tion Imaging Spectrometer (MODIS) on board Terra av- eraged over a 0.1 ^◦ region centered on SIRTA, is 242 K.

Retrievals based on three wavelength constraints yield particle sizes (effective radius) ranging from 8–57 µm.

When the shape constraint is added the range of possi- ble particle size is reduced to 11–35 µm.

The retrieval of the effective size by combination of in- frared radiances and lidar (backscatter and depolariza- tion) has been tested against twenty cases of ice clouds observed with the LNA lidar and the MODIS/TERRA infrared space-borne radiometer. The use of lidar data allows particle size for semi-transparent clouds to be re- trieved and so extends the possible scope of applica- tion of the split-window technique to a wider number of cloud cases. Furthermore, the uncertainty on particle size retrievals is reduced between 20 and 65% when the shape constraint is used (Chiriaco et al., 2004).

The capability and limitations of this method are cur- rently evaluated by comparing the results with in-situ observations from the Cirrus Regional Study of Trop- ical Anvils and Cirrus Layers – Florida Area Cirrus Experiment (CRYSTAL-FACE) and with multi-channel passive remote sensing techniques (Minnis et al., 1998) on a systematic basis during MODIS overpass on the SIRTA. This method will be applied to future CALIPSO lidar and infrared radiometer observations, and there- fore provides particle size in semi-transparent cirrus clouds at the global scale.

• Cloud particle size and ice water content are derived from combined radar/lidar retrievals. As shown in pre- vious studies (e.g. Intrieri et al., 1993), cloud radar and lidar observations carry complementary information on cloud properties, in particular, ice water content and effective radius. Direct methods that link the ratio of radar reflectivity to lidar extinction to the effective ra- dius work well for optical depths less than 0.3, but as- sumptions are not satisfied for thicker clouds. New al- gorithms use the radar reflectivity to better constrain the lidar extinction retrieval (e.g. Donovan et al., 2001;

Tinel et al., 2004). The algorithm developed by the CETP radar team uses a statistical relationship between lidar extinction and radar attenuation derived from a large set of in-situ microphysical observations. It is well known that the cloud-to-cloud variability of the parti- cle size distribution (PSD) is high. However, Testud et al. (2001) showed that for precipitating events the shape of the PSD is roughly invariant when normalizing the diameter axis by the mean diameter of the distribution, that is, the number concentration can be expressed as N (D) = N 0 ∗ F (D/Dm) , (1)

where D is the particle diameter, Dm is the mean diameter of the distribution, and N ₀ ^∗ is the intercept parameter of the PSD. If the shape of the distribu- tion is fixed, all the variability of the PSD is con- tained in N ₀ ^∗ . This normalization concept has been ap- plied to non-precipitating ice clouds by processing very large in-situ microphysical data-sets both in the tropi- cal and mid-latitude regions, in the Northern and South- ern Hemispheres (e.g. the Fronts and Atlantic Storm- Track Experiment, FASTEX; the Central Equatorial Pa- cific Ocean Experiment, CEPEX; the European cloud radar and lidar experiments CLARE-1998, CARL-2000 and CARL-2001). The shape of the normalized PSD is found to be roughly invariant for non-precipitating ice clouds (Delano¨e et al., 2005 ¹ ). As a result, statistical relationships between moments of the normalized PSD are also found to be invariant. The n-th moment of the PSD is defined as

M(n) = Z

N (D) ^D

dD, (2)

where N (D) is the PSD. Since lidar extinction and radar attenuation are proportional to the second and first mo- ments of the PSD, it implies that a “universal” statistical relationship parameterized by N 0 ∗can be derived as

α = aN ₀ ^∗1−b K ^b , (3)

where a and b are the parameters of the relationship, α is the lidar extinction and K is the radar attenua- tion (Delano¨e et al., 2005 ¹ ). N ₀ ^∗ becomes an unknown to be retrieved by the radar-lidar algorithm. This al- gorithm has been validated through dedicated airborne campaigns during the European CARL program, us- ing the airborne RALI radar-lidar system (Protat et al., 2004) flying above the clouds, and airborne in-situ mi- crophysical measurements within the cloud (Tinel et al., 2004).

The radar-lidar algorithm has been applied to the cloud depth seen by the two instruments on 1 April 2003, shown in Fig. 4. Figures 6a, b, c, d show the lidar ex- tinction, ice water content, effective radius, and N ₀ ^∗ , re- spectively. The vertical distribution of the cloud proper- ties is interesting in this case, with ice water content in- creasing, effective radius decreasing, and N ₀ ^∗ increasing with height. The increase in effective radius when ap- proaching the cloud base likely reflects the importance of the aggregation/coalescence processes when cloud particles sediment within the cloud. This is consistent with the retrieved decrease of N ₀ ^∗ when approaching the cloud base, that can be interpreted as a decrease in num- ber of the smaller cloud particles and then as an occur- rence of aggregation. The smaller ice water contents

1 Delano¨e, J., Protat, A., Testud, J., and Bouniol, D.: Statistical

properties of the normalized ice particle size distribution, J. Geo-

phys. Res., under revision, 2004.

Fig. 6. Cloud microphysical properties retrieved from the radar-lidar algorithm on 1 April 2003: (a) lidar extinction coefficient (km ⁻¹ sr ⁻¹ ), (b) ice water content (gm ⁻³ ), (c) particle effective radius (mm), (d) intercept of particle size distribution No*.(m ⁻⁴ ).

at cloud base are likely associated with strong evapo- ration occurring there. Presently, this algorithm is be- ing implemented as a routine algorithm, in order to run continuously on the CloudNet data set and prepare its integration for the automated processing of the future space-borne observations from CALIPSO and Cloud- Sat).

4.2.3 Cirrus cloud dynamical properties

In operational forecast and climate models the life cycle of a cloud is strongly linked to its internal dynamics (sedimen- tation of cloud particles), to the environmental air dynamics (wind) and to the feedbacks between dynamics and micro- physics, that is, to the way the effective radius and water content is modified by the internal cloud dynamics and vice- versa. In a model this essentially translates into two dynamic parameters that must be accurately represented: the terminal fall speed of the cloud particles and the vertical air velocity.

Vertically-pointing cloud radars measure the sum of vertical air velocity w and terminal fall velocity V _T . In order to sep- arate these two components, statistical approaches have been proposed (Orr and Kropfli, 1999; Protat et al., 2002; Protat et al., 2003), assuming that for a long time span the mean vertical air motion should vanish with respect to the mean terminal fall velocity that is much less fluctuating. A statis- tical power-law relationship between the terminal fall speed

and radar reflectivity may therefore be derived using this as- sumption. V _T can then be subtracted from the Doppler ve- locity to access the vertical air velocity component.

The retrieval of cloud dynamics is illustrated in Fig. 7, which shows the retrieved terminal fall velocity and verti- cal air velocity for the 1 April 2003 case. Terminal fall ve- locities range between 0 and −1.0 m/s, which are realistic values for this type of cloud. In the cirrus part of the cloud (07:00–12:00 UT) particles fall in the fall streaks at about 0.5 m/s and about half that velocity outside the fall streaks.

The effect of aggregation is shown in the cumulonimbus part of the cloud (13:00–15:00 UT), with fall velocities in excess of 1.0 m/s in the lowest part of the cloud. Figure 7c reveals positive vertical air velocities close to the cloud top, ranging between + 0.5 and + 1.0 m/s. These updrafts allow particles to reside and grow by vapour deposition. At the top of the cumulonimbus cloud high frequency ascendance and subsi- dence structure (+/ − 1 m/s) reflects the turbulent nature of the dynamics near cloud edges.

The method used here generally works very well for thin

clouds, such as cirrus clouds. However, it is also observed

that a single V _T -reflectivity relationship cannot, in some

cases, be derived to represent accurately the whole cloud

depth, especially when the cloud depth increases, owing to

changes in the cloud microphysics as the crystals fall within

the cloud layer (e.g. aggregation which tends to produce

less and less dense cloud ice particles). Truncating the cloud

Fig. 7. Dynamical properties of the cloud layers observed at SIRTA on 1 April 2003: (a) RASTA radar reflectivity (dBZ), (b) particle terminal fall velocity (m s ⁻¹ ) and (c) air vertical velocity (m s ⁻¹ ).

in several layers and applying the same statistical approach leads to an increase in the statistical error of the V _T esti- mates (Orr and Kropfli, 1999). Therefore, to improve the V _T estimate, we are investigating the use of lidar informa- tion as an additional constraint to the problem. As described in Sect. 4.2.2 (and in Noel et al., 2002), the lidar depolariza- tion ratio carries quantitative information on particle shape (ratio of the crystal length to the diameter of its hexagonal base) under some assumptions. The next step of this study is

to establish an individual Vt-reflectivity relationship for each class of particle shape, which should limit the impact of a change in cloud microphysics as a function of cloud depth.

4.3 Boundary-layer cloud case study

Representation of turbulent and mesoscale transport in

the planetary boundary layer (PBL) is an important issue

for climate modeling, in particular for determination of

Fig. 8. Clouds and aerosols observed during the 26–28 May 2003 period. (a) Range-corrected backscattered lidar signal. Clouds at the top of the boundary layer appear in red (strongest backscattering). A persisting aerosol layer drops from 6 to 3 km from 26 to 28 May. (b) Vertical structure mask of the atmosphere derived from 532-nm lidar backscattering signal. The boundary-layer height is shown in green, aerosol and cloud layers are labeled in orange and red, respectively. Black dots show cloud base height retrieved by ceilometer. Particle-free layers are shown in blue.

biosphere-atmosphere exchanges and for prediction of cloud cover. The fundamental features of the PBL dynamics are its diurnal evolution and its turbulent nature (Stull, 1988).

At night a stably stratified boundary layer lies below a resid- ual layer (RL). During daytime, strong turbulence mixing oc- curs in the convective boundary layer (CBL). In the CBL, the

vertical mixing is non local as opposed to the surface layer,

i.e. mesoscale structures, such as thermals, convective PBL

cells or rolls, transport most of the energy at the various ver-

tical levels (e.g. Emanuel, 1991). In the following case study

we present a method that confronts observations of bound-

ary layer parameters to diagnostic-performed outputs from a